There are known driving support apparatuses making various controls during driving, especially when driving on the freeway or highway. There are also many known methods that support lane marking and/or lane recognition. It is necessary to accurately estimate positions of lane lines in order to appropriately drive a vehicle along the lane lines. Automotive imaging is a recent trend of research to assist drivers that may ultimately result in a driver-less car. Along with state-of-art algorithm, state-of-art validation framework is a requirement to ensure system quality.
In automotive imaging, an offline video marking ground-truth determination is a tedious task by the tester or user. Typically a camera captures in the frequency of 30 image frames per second and marking these manually is a tedious task. Further, scenarios like sudden variation in the environment, sudden inclusion of a foreign object on the lane marks, and multiple lanes in the field of view of the camera are the major challenges. Since ground truth value(s) will be the reference to judge the accuracy of lane departure applications, even minor errors are not acceptable. Available binarization methods may not be compatible with the aforementioned problem scenarios. Hence, the tester should be the supervisor of the automated algorithm and may be able to switch to manual implementation whenever required.